Journal ArticleDOI
Applications of Structural Equation Modeling in Psychological Research
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This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years and focuses first on the variety of research designs and substantive issues to which SEM can be applied productively.Abstract:
This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.read more
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Principles and practice in reporting structural equation analyses.
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A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example
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An introduction to latent class growth analysis and growth mixture modeling.
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References
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Journal ArticleDOI
Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives
Li-tze Hu,Peter M. Bentler +1 more
TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Book
Principles and Practice of Structural Equation Modeling
TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Journal ArticleDOI
Alternative Ways of Assessing Model Fit
Michael W. Browne,Robert Cudeck +1 more
TL;DR: In this paper, two types of error involved in fitting a model are considered, error of approximation and error of fit, where the first involves the fit of the model, and the second involves the model's shape.
Book
Structural Equations with Latent Variables
TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Journal ArticleDOI
Significance tests and goodness of fit in the analysis of covariance structures
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
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Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives
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